Vertica’s recommendations for AWS deployments.
What too look for and check when deploying Vertica on AWS.
You can see the video here:
https://youtu.be/sSkWJ_Afhs4
Introduction to Vertica (Architecture & More)LivePerson
LivePersonDev is happy to host this meetup with Zvika Gutkin, an Oracle and Vertica expert DBA in LivePerson, and specialist in BI and Big Data.
At LivePerson, we handle enormous amounts of data. We use Vertica to analyse this data in real time.
In this lecture Zvika will cover the following:
1. Present the architecture of Vertica
2. Compare row store to column store
3. Explain how Vertica achieve Fast query time
4. Show few use cases .
5. Explains what does Liveperson do with Vertica? Why we chose Vertica?
6. Talk about why we Love Vertica and Why we hate it .
7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent?
8. How Vertica differ from other SQL and noSQL technologies?
In this lecture I will cover the following:
1. Present the architecture of Vertica
2. Compare row store to column store
3. Explain how Vertica achieve Fast query time
4. Show few use cases .
5. Explains what does Liveperson do with Vertica? Why we chose Vertica?
6. Talk about why we Love Vertica and Why we hate it .
7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent?
8. How Vertica differ from other SQL and noSQL technologies?
In this session, we'll review the features and architecture of the new AWS Data Pipeline service and explain how you can use it to better manage your data-driven workloads. We'll then go over a few examples of setting up and provisioning a pipeline in the system.
Data Warehousing with Amazon Redshift: Data Analytics Week SFAmazon Web Services
Data Analytics Week at the San Francisco Loft
Data Warehousing with Amazon Redshift
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWSA closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Speakers:
Jay Formosa - Solutions Architect, AWS
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
See a recording of the webinar based on this presentation here on YouTube: https://youtu.be/GgLKodmL5xE
Masterclass series webinars, including on-demand access to all of this years recorded webinars: http://aws.amazon.com/campaigns/emea/masterclass/
Journey Through the Cloud webinar series, including on-demand access to all webinars so far this year: http://aws.amazon.com/campaigns/emea/journey/
Vertica’s recommendations for AWS deployments.
What too look for and check when deploying Vertica on AWS.
You can see the video here:
https://youtu.be/sSkWJ_Afhs4
Introduction to Vertica (Architecture & More)LivePerson
LivePersonDev is happy to host this meetup with Zvika Gutkin, an Oracle and Vertica expert DBA in LivePerson, and specialist in BI and Big Data.
At LivePerson, we handle enormous amounts of data. We use Vertica to analyse this data in real time.
In this lecture Zvika will cover the following:
1. Present the architecture of Vertica
2. Compare row store to column store
3. Explain how Vertica achieve Fast query time
4. Show few use cases .
5. Explains what does Liveperson do with Vertica? Why we chose Vertica?
6. Talk about why we Love Vertica and Why we hate it .
7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent?
8. How Vertica differ from other SQL and noSQL technologies?
In this lecture I will cover the following:
1. Present the architecture of Vertica
2. Compare row store to column store
3. Explain how Vertica achieve Fast query time
4. Show few use cases .
5. Explains what does Liveperson do with Vertica? Why we chose Vertica?
6. Talk about why we Love Vertica and Why we hate it .
7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent?
8. How Vertica differ from other SQL and noSQL technologies?
In this session, we'll review the features and architecture of the new AWS Data Pipeline service and explain how you can use it to better manage your data-driven workloads. We'll then go over a few examples of setting up and provisioning a pipeline in the system.
Data Warehousing with Amazon Redshift: Data Analytics Week SFAmazon Web Services
Data Analytics Week at the San Francisco Loft
Data Warehousing with Amazon Redshift
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWSA closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Speakers:
Jay Formosa - Solutions Architect, AWS
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
See a recording of the webinar based on this presentation here on YouTube: https://youtu.be/GgLKodmL5xE
Masterclass series webinars, including on-demand access to all of this years recorded webinars: http://aws.amazon.com/campaigns/emea/masterclass/
Journey Through the Cloud webinar series, including on-demand access to all webinars so far this year: http://aws.amazon.com/campaigns/emea/journey/
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing and scale-out architecture to ensure compute resources grow with your dataset size, and columnar, direct-attached storage to dramatically reduce I/O time. Learn how top online retailer RetailMeNot moved their largest Vertica cluster on Amazon EC2 to Amazon Redshift. See how they gain insights from clickstream, location, merchant, marketing, and operational data across desktop and mobile properties.
Best Practices for Migrating Your Data Warehouse to Amazon RedshiftAmazon Web Services
by Darin Briskman, Technical Evangelist, AWS
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms. Level: 200
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms.
Streaming is necessary to handle data rates and latency but SQL is unquestionably the lingua franca of data. Where do the two meet?
Apache Calcite is extending SQL to include streaming, and the Samza, Storm and Flink are projects are each building it into their engines. In this talk, Julian Hyde describes streaming SQL in detail and shows how you can use streaming SQL in your application. He also describes how Calcite’s planner optimizes queries for throughput and latency.
Julian Hyde gave this talk at the first Kafka Summit, San Francisco, 2016/04/26.
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...Amazon Web Services
Get a look under the hood: Understand how to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. You’ll also hear about how the University of Technology Sydney (UTS) are using Redshift. The University of Technology Sydney will describe how utilizing Amazon Redshift enabled agility in dealing with Data Quality, a capacity to scale when required, and optimizing development processes through rapid provisioning of Data Warehouse environments.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services with Susan Gibson, Manager, Data and Business Intelligence, UTS
Level: 300
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and use work load management.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Who Should Attend:
• Data Warehouse Developers, Big Data Architects, BI Managers, and Data Engineers
Introduction to Amazon Redshift and What's Next (DAT103) | AWS re:Invent 2013Amazon Web Services
Amazon Redshift is a fast, fully-managed, petabyte-scale data warehouse service that costs less than $1,000 per terabyte per year—less than a tenth the price of most traditional data warehousing solutions. In this session, you get an overview of Amazon Redshift, including how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. Finally, we announce new features that we've been working on over the past few months.
by Edin Zulich, NoSQL Solutions Architect, AWS
Explore Amazon DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over best practices for schema design with DynamoDB across multiple use cases, including gaming, IoT, and others. We explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including DynamoDB Accelerator (DAX), DynamoDB Time-to-Live, and more. We also provide lessons learned from operating DynamoDB at scale, including provisioning DynamoDB for IoT. Level: 200
Discardable In-Memory Materialized Queries With HadoopJulian Hyde
What to do with all that memory in a Hadoop cluster? Should we load all of our data into memory to process it?
The goal should be to put memory into its right place in the storage hierarchy, alongside disk and solid-state drives (SSD). Data should reside in the right place for how it is being used, and should be organized appropriately for where it resides. This proposed solution requires a new kind of data set called the Discardable, In-Memory, Materialized Query (DIMMQ).
In this session we will talk through how we can build on existing Hadoop facilities to deliver three key underlying concepts that enable this approach.
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
AWS July Webinar Series: Amazon Redshift Optimizing PerformanceAmazon Web Services
Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze your data for a fraction of the cost of traditional data warehouses.
By following a few best practices for schema design and cluster design, you can unleash the high performance capabilties of Amazon Redshift. This webinar is a deep dive into performance tuning techniques based on real-world use cases.
Learning Objectives:
Learn how to get the best performance from your Redshift cluster
Design Amazon Redshift clusters based on real world use cases
See sample tuning scripts to diagnose and maximize cluster performance
Learn about increasing query performance using interleaved sorting
Deep Dive Amazon Redshift for Big Data Analytics - September Webinar SeriesAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and tune query and database performance.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Large-Scaled Telematics Analytics in Apache Spark with Wayne Zhang and Neil P...Databricks
The increasing availability of mobile phones with embedded GPS devices and sensors has spurred the use of vehicle telematics in recent years. Telematics provides detailed and continuous information of a vehicle such as the location, speed, and movement. Vehicle telematics can be further linked with other spatial data to provide context to understand driving behaviors. The collection of high-frequency telematics data results in huge volumes of data that must be processed efficiently. We present a solution that uses Apache Spark to load and transform large-scaled telematics data. We then present how to use machine learning on telematics data to derive insights about driving safety.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing and scale-out architecture to ensure compute resources grow with your dataset size, and columnar, direct-attached storage to dramatically reduce I/O time. Learn how top online retailer RetailMeNot moved their largest Vertica cluster on Amazon EC2 to Amazon Redshift. See how they gain insights from clickstream, location, merchant, marketing, and operational data across desktop and mobile properties.
Best Practices for Migrating Your Data Warehouse to Amazon RedshiftAmazon Web Services
by Darin Briskman, Technical Evangelist, AWS
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms. Level: 200
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms.
Streaming is necessary to handle data rates and latency but SQL is unquestionably the lingua franca of data. Where do the two meet?
Apache Calcite is extending SQL to include streaming, and the Samza, Storm and Flink are projects are each building it into their engines. In this talk, Julian Hyde describes streaming SQL in detail and shows how you can use streaming SQL in your application. He also describes how Calcite’s planner optimizes queries for throughput and latency.
Julian Hyde gave this talk at the first Kafka Summit, San Francisco, 2016/04/26.
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...Amazon Web Services
Get a look under the hood: Understand how to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. You’ll also hear about how the University of Technology Sydney (UTS) are using Redshift. The University of Technology Sydney will describe how utilizing Amazon Redshift enabled agility in dealing with Data Quality, a capacity to scale when required, and optimizing development processes through rapid provisioning of Data Warehouse environments.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services with Susan Gibson, Manager, Data and Business Intelligence, UTS
Level: 300
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and use work load management.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Who Should Attend:
• Data Warehouse Developers, Big Data Architects, BI Managers, and Data Engineers
Introduction to Amazon Redshift and What's Next (DAT103) | AWS re:Invent 2013Amazon Web Services
Amazon Redshift is a fast, fully-managed, petabyte-scale data warehouse service that costs less than $1,000 per terabyte per year—less than a tenth the price of most traditional data warehousing solutions. In this session, you get an overview of Amazon Redshift, including how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. Finally, we announce new features that we've been working on over the past few months.
by Edin Zulich, NoSQL Solutions Architect, AWS
Explore Amazon DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over best practices for schema design with DynamoDB across multiple use cases, including gaming, IoT, and others. We explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including DynamoDB Accelerator (DAX), DynamoDB Time-to-Live, and more. We also provide lessons learned from operating DynamoDB at scale, including provisioning DynamoDB for IoT. Level: 200
Discardable In-Memory Materialized Queries With HadoopJulian Hyde
What to do with all that memory in a Hadoop cluster? Should we load all of our data into memory to process it?
The goal should be to put memory into its right place in the storage hierarchy, alongside disk and solid-state drives (SSD). Data should reside in the right place for how it is being used, and should be organized appropriately for where it resides. This proposed solution requires a new kind of data set called the Discardable, In-Memory, Materialized Query (DIMMQ).
In this session we will talk through how we can build on existing Hadoop facilities to deliver three key underlying concepts that enable this approach.
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
AWS July Webinar Series: Amazon Redshift Optimizing PerformanceAmazon Web Services
Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze your data for a fraction of the cost of traditional data warehouses.
By following a few best practices for schema design and cluster design, you can unleash the high performance capabilties of Amazon Redshift. This webinar is a deep dive into performance tuning techniques based on real-world use cases.
Learning Objectives:
Learn how to get the best performance from your Redshift cluster
Design Amazon Redshift clusters based on real world use cases
See sample tuning scripts to diagnose and maximize cluster performance
Learn about increasing query performance using interleaved sorting
Deep Dive Amazon Redshift for Big Data Analytics - September Webinar SeriesAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and tune query and database performance.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Large-Scaled Telematics Analytics in Apache Spark with Wayne Zhang and Neil P...Databricks
The increasing availability of mobile phones with embedded GPS devices and sensors has spurred the use of vehicle telematics in recent years. Telematics provides detailed and continuous information of a vehicle such as the location, speed, and movement. Vehicle telematics can be further linked with other spatial data to provide context to understand driving behaviors. The collection of high-frequency telematics data results in huge volumes of data that must be processed efficiently. We present a solution that uses Apache Spark to load and transform large-scaled telematics data. We then present how to use machine learning on telematics data to derive insights about driving safety.
Optimize Your Vertica Data Management InfrastructureImanis Data
Slides from our webinar presented by, Talena’s Chief Architect, Srinivas Vadlamani, covering several key questions that arise when looking to optimize your HPE Vertica infrastructure to minimize data loss, support rapid application iteration and ensure compliance with critical internal and external mandates.
The cluster-based, column-oriented, Vertica Analytics Platform is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications.Support for standard programming interfaces ODBC, JDBC, and ADO.NET.The Vertica Analytic Database runs on cluster of Linux-based commodity servers. It is also available as a hosted DBMS provisioned by and running on the Amazon Elastic Compute Cloud. The product integrates with Hadoop
Carlos González, Hewlett Packard Enterprise, nos habla acerca en la implicación del mercado de Big Data en su negocio y el papel que una solución como Vertica juega en éste de la mano de Qlik.
Big Data Day LA 2015 - Scalable and High-Performance Analytics with Distribut...Data Con LA
"R is the most popular language in the data-science community with 2+ million users and 6000+ R packages. R’s adoption evolved along with its easy-to-use statistical language, graphics, packages, tools and active community. In this session we will introduce Distributed R, a new open-source technology that solves the scalability and performance limitations of vanilla R. Since R is single-threaded and does not scale to accommodate large datasets, Distributed R addresses many of R’s limitations. Distributed R efficiently shares sparse structured data, leverages multi-cores, and dynamically partitions data to mitigate load imbalance.
In this talk, we will show the promise of this approach by demonstrating how important machine learning and graph algorithms can be expressed in a single framework and are substantially faster under Distributed R. Additionally, we will show how Distributed R complements Vertica, a state-of-the-art columnar analytics database, to deliver a full-cycle, fully integrated, data “prep-analyze-deploy” solution."
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
In this session we will look at the options for replicating content between Alfresco repositories. Starting with a re-cap of the existing functionality of version 3.3, we will then introduce the new replication features of Alfresco 3.4 including some more advanced scenarios. If you have been paying attention to recent SVN commits then you can't have failed to notice that Alfresco folders can be invaded by aliens. Find out what that means in this session!
ApacheCon 2020 - Flink SQL in 2020: Time to show off!Timo Walther
Four years ago, the Apache Flink community started adding SQL support to ease and unify the processing of static and streaming data. Today, Flink runs business critical batch and streaming SQL queries at Alibaba, Huawei, Lyft, Uber, Yelp, and many others. Although the community made significant progress in the past years, there are still many things on the roadmap and the development is still speeding up. In the past months, several significant improvements and extensions were added including support for DDL statements, refactorings of the type system and the catalog interface, as well as Apache Hive integration. Since it is difficult to follow all development efforts that happen around Flink SQL and its ecosystem, it is time for an update. This session will focus on a comprehensive demo of what is possible with Flink SQL in 2020. Based on a realistic use case scenario, we'll show how to define tables which are backed by various storage systems and how to solve common tasks with streaming SQL queries. We will demonstrate Flink's Hive integration and show how to define and use user-defined functions. We'll close the session with an outlook of upcoming features.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/36epVKg.
Todd Montgomery discusses the techniques and lessons learned from implementing Aeron Cluster. His focus is on how Raft can be implemented on Aeron, minimizing the network round trip overhead, and comparing single process to a fully distributed cluster. Filmed at qconsf.com.
Todd Montgomery is a networking hacker who has researched, designed, and built numerous protocols, messaging-oriented middleware systems, and real-time data systems, done research for NASA, contributed to the IETF and IEEE, and co-founded two startups. He currently works as an independent consultant and is active in several open source projects.
Replicate from Oracle to data warehouses and analyticsContinuent
Analyzing transactional data residing in Oracle databases is becoming increasingly common, especially as the data sizes and complexity increase and transactional stores are no longer to keep pace with the ever-increasing storage. Although there are many techniques available for loading Oracle data, getting up-to-date data into your data warehouse store is a more difficult problem. VMware Continuent provides provides data replication from Oracle to data warehouses and analytics engines, to derive insight from big data for better business decisions. Learn practical tips on how to get your data warehouse loading projects off the ground quickly and efficiently when replicating from Oracle into Hadoop, Amazon Redshift, and HP Vertica.
In this talk, we go over the history and future of Apache Flink adoption at Shopify. We'll talk about how and why we went from choosing Apache Flink as the replacement for our existing streaming technologies in 2021, to a year later with a flourishing streaming community. Today, we have tens of prototypes and several large use-cases running production. Along the way, we'll overview the Flink ecosystem at Shopify, the tools and libraries Shopify built, the decision to fork Flink, how we drove adoption of streaming at the company, and what's next for the platform.
Apache Flink Adoption at Shopify With Kevin Lam | Current 2022HostedbyConfluent
In this talk, we go over the history and future of Apache Flink adoption at Shopify.
We’ll talk about how and why we went from choosing Apache Flink as the replacement for our existing streaming technologies in 2021, to a year later with a flourishing streaming community. Today, we have tens of prototypes and several large use-cases running production.
Along the way, we’ll overview the Flink ecosystem at Shopify, the tools and libraries Shopify built, the decision to fork Flink, how we drove adoption of streaming at the company, and what’s next for the platform.
Running Kubernetes in Production: A Million Ways to Crash Your Cluster - Cont...Henning Jacobs
Bootstrapping a Kubernetes cluster is easy, rolling it out to nearly 200 engineering teams and operating it at scale is a challenge. In this talk, we are presenting our approach to Kubernetes provisioning on AWS, operations and developer experience for our growing Zalando developer base. We will walk you through our horror stories of operating 80+ clusters and share the insights we gained from incidents, failures, user reports and general observations. Most of our learnings apply to other Kubernetes infrastructures (EKS, GKE, ..) as well. This talk strives to reduce the audience’s unknown unknowns about running Kubernetes in production.
https://2018.container.camp/uk/schedule/running-kubernetes-in-production-a-million-ways-to-crash-your-cluster/
Build a Bridge to Cloud with Apache Kafka® for Data Analytics Cloud Servicesconfluent
Build a Bridge to Cloud with Apache Kafka® for Data Analytics Cloud Services, Perry Krol, Head of Systems Engineering, CEMEA, Confluent
https://www.meetup.com/Frankfurt-Apache-Kafka-Meetup-by-Confluent/events/269751169/
Maximize Application Performance and Bandwidth Efficiency with WAN OptimizationCisco Enterprise Networks
Learn how a two-step strategy that reduces application bandwidth consumption and makes more efficient use of your remaining bandwidth can help you achieve seemingly conflicting business and IT goals.
Register to watch webcast: http://cs.co/9006CAY0.
A rough and researchy presentation where I tried out some new material in front of a local audience. Skipped the usual introduction and talked about some of the problems people run into when they do microservices and miss a few things. More refined version of this talk to be shown at O'Reilly Software Architecture Conference in New York in April.
Forward Networks - Networking Field Day 13 presentationAndrew Wesbecher
On November 17th, 2016, Forward Networks conducted its first public unveiling of its Network Assurance platform at Networking Field Day 13. Visit https://www.forwardnetworks.com/ for more details.
Highilights from Rod Randall (SIRIS/Stratus) LTE AsiaAlan Quayle
Highlights from Rod Randall's presentation on the move of telco software to the cloud. SIRIS has recently purchased Stratus Technologies, given their purchase and then sale of Tekelec, this is one to watch. Its a fault tolerant hypervisor, statepoint. Also Rod had some nice slides that highlights the challenges facing NFV/SDN.
An overview of project Skyfall. A globally distributed fault tolerant event consumption framework used by AddThis.com to consume billions of events per day.
Replication in real-time from Oracle and MySQL into data warehouses and analy...Continuent
Analyzing transactional data is becoming increasingly common, especially as the data sizes and complexity increase and transactional stores are no longer to keep pace with the ever-increasing storage. Although there are many techniques available for loading data, getting effective data in real-time into your data warehouse store is a more difficult problem. VMware Continuent provides capabilities for continuous and real-time data warehouse loading. Join us for practical tips and a live demo of how to get your data warehouse loading projects off the ground quickly and efficiently when replicating from MySQL and Oracle into Amazon Redshift, HP Vertica and Hadoop
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
9. Pro Tip
HDFS Vs.
Hadoop Vs.
Application
Loader
Big Bulks
Bring your
files to your
cluster
Load from
Several Nodes
Convertro
Pro Tip Pro Tip
Vertica The Convertro wa
33. Convertro
Many
Deletes / Updates
Node
Crash
Slow Recover
Process
Checking Recovery
Status
Incremental
recovery
replay-
delete
Solution 3
Delete only
one file
Incremental By Containers
Vertica The Convertro wa
The data for the model is in vertica
Process in R
Attribution run on hadoop based on the model that R calculated .
Vertica :
Raw : 150TB
DB Size : 60 TB
Top Clients: (top 8 above 1 TB )
Intuit 3 TB
qvc 2 TB
Dashboard :
qvc => 260 GB => 8 Billion rows
intuit => 200 GB => less than 8 Billion rows Vertica scan it and fetches a result in less than a second
Complex analytics on a year takes about 5 seconds.
Facebook 35 TB per hour 2 years ago !!
150K rows per sec
Regular days 10 – 15 billion rows per day
40 billion rows per day
New vertica feature => Vhash
out of the box improvements =>
denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
out of the box improvements =>
denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
MMM => measure measure measure => data collector tables .
out of the box improvements =>
denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
MMM => measure measure measure => data collector tables .
out of the box improvements =>
denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
MMM => measure measure measure => data collector tables .
out of the box improvements =>
denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
MMM => measure measure measure => data collector tables .
out of the box improvements =>
denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
MMM => measure measure measure => data collector tables .
great database => out of the box performance
even lebron => don’t put billing on it
right tool => hadoop loader,extended analytics ,flex table,udx
Keep it simple => easy to debug easy to maintain
great database => out of the box performance
even lebron => don’t put billing on it
right tool => hadoop loader,extended analytics ,flex table,udx
Keep it simple => easy to debug easy to maintain
great database => out of the box performance
even lebron => don’t put billing on it
right tool => hadoop loader,extended analytics ,flex table,udx
Keep it simple => easy to debug easy to maintain